DCT Technique Research Articles

Recognition Of Digits From EEG Signals: An Innovative Approach

Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent of deep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. This study proposes a methodological combination of EEG signal processing techniques and SVM models for the classification of digit. Also, it explores DCT and LDA techniques for feature extraction and selection. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.

Dual Level Strategy Integrating 2-D CA and DCT Technique for Enhanced Data Protection

Dual Level Strategy Integrating 2-D CA and DCT Technique for Enhanced Data Protection

Role of Compressed Medical Images In Healthcare

This paper will focus on techniques used in patients monitoring in healthcare based on medical images. Lossless coding algorithm, watermark compression (add safety to data), GLCM, Huffman coding, LZW, DCT and soft compression techniques are used for efficient image compression. Lossless coding algorithms helps to reduce image size without losing the data. An image watermarking secret key (ROI) is used in this research to create a watermark that is unique to each picture. By comparing LZW compression with traditional approaches, it was observed that the LZW methodology has better performance than others.

A Robust Algorithm for Digital Image Copyright Protection and Tampering Detection: Employing DWT, DCT, and Blowfish Techniques

A Robust Algorithm for Digital Image Copyright Protection and Tampering Detection: Employing DWT, DCT, and Blowfish Techniques

Human Recognition Using Ear Features: A Review

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as DCT, DWT, DFT, PCA, LBP, SURF, SIFT, etc., or deep learning techniques such as CNN, DNN, Alex Net CNN, VGG-16, SVM, Squeeze Net, Google Net, MobileNetV2, etc. The effort will make it easier for researchers, especially those who are new to the field, to have a brief understanding of the trend of employing deep learning in a trustworthy biometric for the identification and recognition of human identification.

Registration between DCT and EBSD datasets for multiphase microstructures

The ability to characterise the three-dimensional microstructure of multiphase materials is essential for understanding the interaction between phases and their associated materials properties. Here, laboratory-based diffraction-contrast tomography (lab-based DCT), a recently-established materials characterization technique that can determine grain phases, morphologies, positions and orientations in a voxel-based reconstruction method, was used to map part of a dual-phase steel alloy sample. To assess the resulting microstructures produced by the lab-based DCT technique, an electron backscatter diffraction (EBSD) map was collected within the same sample volume. To identify the two-dimensional (2D) slice of the three-dimensional (3D) lab-based DCT reconstruction that best corresponded to the 2D EBSD map, a novel registration technique based solely on grain-averaged orientations was developed – this registration technique requires very little a priori knowledge of dataset alignment and can be extended to other techniques that only recover grain-averaged orientation data such as far-field 3D X-ray diffraction microscopy. Once the corresponding 2D slice was identified in the lab-based DCT dataset, comparisons of phase balance, grain size, shape and texture were performed between lab-based DCT and EBSD techniques. More complicated aspects of the microstructural morphology such as grain boundary shape and grains less than a critical size were poorly reproduced by the lab-based DCT reconstruction, primarily due to the difference in resolutions of the technique compared with EBSD. However, lab-based DCT is shown to accurately determine the centre-of-mass position, orientation, and size of the large grains for each phase present, austenite and martensitic ferrite. The results reveals a complex ferrite grain network of similar crystal orientations that are absent from the EBSD dataset. Such detail demonstrates that lab-based DCT, as a technique, shows great promise in the field of multi-phase material characterization.

Medical Image Watermarking for Tamper Detection in ROI Using LWT and Hashing

Security is one of the important characteristics while transmitting the data particularly medical images and patient information over cyberspace without any loss of information. In the proposed method LWT, DCT and SHA-256 hashing techniques are used to provide security to the patient data and to detect tampers in ROI of the medical images. In this paper, medical image is segmented into three regions, region of interest (ROI), region of non interest (RONI) and BORDER regions. ROI is an area that has an important impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper proposed ROI based tamper detection that embeds hash value of ROI into RONI for authentication and also embed the patient information into RONI for providing security. The experimental results of the proposed method on various medical image databases proved to identify the tampers in medical images. Compared with the existing technique, the proposed method offered high Peak Signal to Noise Ratio (PSNR) approximately 59db for watermarked medical images.

Estimation of Trapping Induced Dynamic Reduction in 2DEG Density of GaN-Based HEMTs by Gate-Lag DCT Technique

We derived a simplified analytical expression to estimate the dynamic reduction in the 2DEG density due to the buffer trapping ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{T}$ </tex-math></inline-formula> ) in GaN-based high-electron-mobility transistors (HEMTs). To compute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{T}$ </tex-math></inline-formula> , gate-lag drain current transient (DCT) measurements are carried out at a fixed temperature for different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${V}_{GS}$ </tex-math></inline-formula> points. The dynamic decrease in the 2DEG due to the Fe-related trap at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}_{C}$ </tex-math></inline-formula> -0.5 eV is found to be ~ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.4\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> in AlGaN/GaN HEMT; thus, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{T}$ </tex-math></inline-formula> is an essential quantity to evaluate the current collapse effects in HEMT. The validity of the expression is further demonstrated in the C-doped HEMTs. The calculated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{T}$ </tex-math></inline-formula> for the trap at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}_{C}$ </tex-math></inline-formula> -0.14 eV is about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−0</sup> in InAlN/GaN HEMT with C-doped buffer, and the calculated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{T}$ </tex-math></inline-formula> for the trap at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}_{C}$ </tex-math></inline-formula> -0.33 eV is ~ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times10$ </tex-math></inline-formula> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> in AlN/GaN HEMT with C-doped buffer. These values reveal that current collapse effects are more pronounced in the C-doped HEMTs than the Fe-doped HEMTs. Thus, this work is an important first step before further improvements in the model for computing buffer trap concentration.

Devanagari handwritten characters recognition using DCT, geometric and hue moments feature extraction techniques

Devanagari handwritten characters recognition using DCT, geometric and hue moments feature extraction techniques

Ownership Protection on Digital Elevation Model (DEM) Using Transform-Based Watermarking

This research aims to protect Digital Elevation Model (DEM) data from piracy or counterfeiting. An invisible watermark inserted into the data, which will not considerably change the data value, is necessary. The proposed method involves the use of the two-dimensional discrete cosine transform (2D DCT), a combination of 2D DCT and discrete wavelet transform (DWT), and two-dimensional discrete Fourier transform (2D DFT) in the frequency domain. The data used include a National DEM file downloaded from the geoportal of the Geospatial Information Agency (Badan Informasi Geospasial—BIG). Three files represent mountainous, lowland/urban, and coastal areas. An “attack” is also conducted on the watermarked DEM by cropping. The results indicate that the watermarked DEM is well recognized. The watermark can be read 100% for 2D DCT, while that for 2D DFT can be read 90.50%. The distortion value of the elevation data under the DCT technique demonstrates the smallest maximum value of 0.1 m compared with 4.5 and 1.1 m for 2D DFT and 2D DCT–DWT. Meanwhile, the height difference (Max Delta), the peak signal-to-noise ratio, and the root mean squared error (RMSE) are highest in mountainous, lowland, and coastal areas, respectively. Overall, the 2D DCT is also superior to the 2D DFT and the2D DCT–DWT. Although only one can recognize the nine watermarks inserted on each sheet, DEMs attacked by the cropping process can still be identified. However, this finding can sufficiently confirm that DEMs belong to BIG.

Optimal Image Steganography Content Destruction Techniques

The paper presents two approaches for destroying steganogrphy content in an image. The first is the overwriting approach where a random data can be written again over steganographic images whereas the second approach is the denoising approach. With the second approach two kinds of destruction techniques have been adopted these are filtering and discrete wavelet techniques. These two approaches have been simulated and evaluated over two types of hiding techniques, Least Significant Bit LSB technique and Discrete Cosine Transform DCT technique. The results of the simulation show the capability of both approaches to destroy the hidden information without any alteration to the cover image except the denoising approach enhance the PSNR in any received image even without hidden information by an average of 4dB.

A novel pooling method for CNN model based on discrete cosine transform

Deep learning can be used to learn huge volume of data, which will be processed through hidden layers and according to the number of hidden layers ,filter size and numbers and the required computation cost is increased because of the size of raw data, this problem can be avoided by using pooling techniques, different method s are proposed to extract the basic features of the signal instead of all signal, but unfortunately this operation may introduce some noise or omission because of elimination important data from the signal. In this paper, A novel pooling method are proposed based on discrete cosine transform , this method is utilized DCT technique to reduce spatial redundancy of image by transform the spatial domain into frequency domain , which can preserve the most significant image information from the other coefficients, which represents the other details information of the image, so discard these less important coefficients. Its effect will be slight and this can reduce the eliminated information as compared with other methods. After applying DCT, we crop the most significant coefficients to be used in the reconstructed data by applying inverse DCT . then the result is combined in different methods with Max pooling and average pooling methods, this new structure can reduce the effect of discarding most important information and reduce the drawbacks of average and Max. pooling method. لإhe results are proved that our proposed methods are outperformed some standard methods and can be used in more application.

A novel pooling method for CNN model based on discrete cosine transform

Deep learning can be used to learn huge volume of data, which will be processed through hidden layers and according to the number of hidden layers ,filter size and numbers and the required computation cost is increased because of the size of raw data, this problem can be avoided by using pooling techniques, different method s are proposed to extract the basic features of the signal instead of all signal, but unfortunately this operation may introduce some noise or omission because of elimination important data from the signal. In this paper, A novel pooling method are proposed based on discrete cosine transform , this method is utilized DCT technique to reduce spatial redundancy of image by transform the spatial domain into frequency domain , which can preserve the most significant image information from the other coefficients, which represents the other details information of the image, so discard these less important coefficients. Its effect will be slight and this can reduce the eliminated information as compared with other methods. After applying DCT, we crop the most significant coefficients to be used in the reconstructed data by applying inverse DCT . then the result is combined in different methods with Max pooling and average pooling methods, this new structure can reduce the effect of discarding most important information and reduce the drawbacks of average and Max. pooling method. لإhe results are proved that our proposed methods are outperformed some standard methods and can be used in more application.

WITHDRAWN: Comparing between DFT and DCT techniques to classification earthquake or explosion

Seismic data is data that is recorded on the Seismographs as a result of the movement of the earth resulting from earthquakes and explosions or any source that causes the movement of the earth. The seismic tremor stations consistently gather information in any event, when there is no occasion. This data is processed data using the techniques used to process digital signals and digital image processing techniques to obtain information that helps in exploring the ground and mining oil…etc. From this information, we have to recognize seismic tremor and non-quake. AI procedures can be utilized to analyze consistent time arrangement information to recognize seismic tremors viably, In this paper we suggested processing seismic data with DFT and DCT techniques and classify it by K-means algorithms in order to distinguish whether these seismic signals are caused by earthquakes or explosions and we obtained the result that DFT technology has a discrimination ratio of its coefficients is 14 of 16 while DCT technology has an error rate of 5 of 16.

Performance Analysis and Improvement of OFDM System Based on BCH Channel Coding and DCT Technique

Performance Analysis and Improvement of OFDM System Based on BCH Channel Coding and DCT Technique

Hybrid Mode of Medical Image Watermarking To Enhance Robustness and Imperceptibility

In Modern healthcare systems, diagnostic information of patients is managed digitally. Watermarking strategies plays a significant role to ensure the security, authenticity and management of biomedical images and related patient information. This paper explores different invisible Digital watermarking methods both in spatial and transform / frequency domain and then Hybrid method by combining two techniques to justify better results for medical images. Here, host image considered is a medical image and patient’s information is a watermark which is embedded into it. The embedded watermark is hidden in medical host image and its quality should not be degraded to avoid misdiagnosis by doctors. In this paper, LSB, SVD, DCT, DWT and Hybrid techniques (DWT+SVD) are implemented for embedding and extraction process of invisible medical image watermarking. The performance is evaluated based on PSNR as a measure of imperceptibility and CRC &amp; SSIM as a check of robustness. The attacks introduced are cropping, rotating, and noise like Gaussian, Salt &amp; Paper, Speckle and Poisson. Results reveal that DWT provides higher PSNR values but it is robust for few attacks only. On the other side, SVD provides consistent SSIM for all attacks, although PSNR values are less than that of DWT. Hence a hybrid technique is implemented by incorporating benefits of DWT and SVD both and achieved optimal values of PSNR as well as SSIM to provide better security to medical images by preserving the quality as well.

Design and Development of Semi-Blind Image Steganography Algorithm using DCT for Securing Text Documents which Resist against JPEG Compression

Semi-blind Image Steganography algorithm development proposed by using DC coefficients of DCT technique. Create KEY vector and potential block matrix while embedding the secret data. Embed one secret character in one DCT block using the DC value of each block. Convert DC coefficient to binary representation and store positions for secret data. Apply JPEG compression on Stego Image. While extracting the secret data from compressed Stego Image, with the use of a KEY vector extracts secret data bits from potential blocks. After creating simulation, perform some test on a standard dataset and compare the results with target results.

Stent Retriever-Assisted Continuous Aspiration for Distal Intracranial Vessel Embolectomy: The Distal Combined Technique

We investigated the efficacy of a combined approach with stent retriever-assisted aspiration catheter for distal intracranial vessel occlusion (distal combined technique [DCT]). We evaluated consecutive patients with acute ischemic stroke with distal occlusion in anterior circulation, including occlusions of the M2/M3 or A2/A3 segments, who received endovascular therapy (EVT) in a single center. Modified Thrombolysis in Cerebral Infraction (mTICI) score including TICI 2C category, processing time from puncture to reperfusion, proportion of a favorable clinical outcome at discharge (modified Rankin Scale [mRS] score ≤2), and incidence of symptomatic intracranial hemorrhage (sICH) were compared between the DCT and single device approach technique (non-DCT) groups. Of 65 patients, 28 were treated with EVT using the DCT and 37 were treated with EVT with a single device approach (non-DCT). In the DCT group, a higher reperfusion rate at the first pass (mTICI score ≥2B, 92% vs. 54%; P= 0.0008; mTICI score ≥2C, 71% vs. 16%; P < 0.0001; mTICI score 3, 57% vs. 14%; P= 0.0004) and shorter time from puncture to successful reperfusion (median, 31 vs. 43 minutes; P= 0.0006) were achieved, respectively. The final successful reperfusion rate was also higher in the DCT group than in the non-DCT group (mTICI score ≥2C, 85% vs. 51%; P= 0.004; mTICI score 3, 75% vs. 43%; P= 0.012), respectively. sICH occurred in 2 patients in the non-DCT group. Patients with mRS score ≤2 at discharge were more prevalent in the DCT than in the non-DCT group (57% vs. 27%, respectively; P= 0.021). This retrospective analysis indicated that the DCT is a useful and safe strategy for patients with distal anterior intracranial vessel occlusion.

Imperceptible and secure image watermarking using DCT and random spread technique

Watermarking is a copyright protection technique, while cryptography is a message encoding technique. Imperceptibility, robustness, and safety are aspects that are often investigated in watermarking. Cryptography can be implemented to increase watermark security. Beaufort cipher is the algorithm proposed in this research to encrypt watermark. The new idea proposed in this research is the utilization of Beaufort key for watermark encryption process as well as for spread watermark when inserted as PN Sequence substitute with the aim to improve imperceptibility and security aspects. Where PN Sequence is widely used in spread spectrum watermarking technique. Based on the experimental results and testing of the proposed method proved that imperceptibility and watermark security are increased. Improved imperceptibility measured by PSNR rose by about 5dB and so did the MSE score better. Robustness aspect is also maintained which has been proven by the excellent value of NCC.

Effect of Secret Image Transformation on the Steganography Process by using LSB &amp; DCT Techniques

Effect of Secret Image Transformation on the Steganography Process by using LSB &amp; DCT Techniques